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SCIENCE CHINA Life Sciences, Volume 64 , Issue 5 : 766-783(2021) https://doi.org/10.1007/s11427-020-1788-2

Gut microbiome alterations and its link to corticosteroid resistance in immune thrombocytopenia

Yanan Wang 1,2,3,†, Fengqi Liu 1,2,3,†, Gaochao Zhang 1,2,3, Yan Su 1,2,3, Xueyan Sun 1,2,3, Qi Chen 1,2,3, Chencong Wang 1,2,3, Haixia Fu 1,2,3, Yun He 1,2,3, Xiaolu Zhu 1,2,3, Xiao Liu 1,2,3, Meng Lv 1,2,3, Xiangyu Zhao 1,2,3, Xiaosu Zhao 1,2,3, Yueying Li 4,5, Qianfei Wang 4,5,6, Xiaojun Huang 1,2,3, Xiaohui Zhang 1,2,3,*
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  • ReceivedJun 2, 2020
  • AcceptedAug 3, 2020
  • PublishedAug 25, 2020

Abstract


Funding

Beijing Municipal Science and Technology Commission(Z171100001017084)

Natural Science Foundation of Beijing Municipality(7171013,H2018206423)

Key Program of National Natural Science Foundation of China(81730004)

the National Natural Science Foundation of China(81670116,81970113)

and National Key Research and Development Program of China(2017YFA0105503)


Acknowledgment

This work was supported by Beijing Municipal Science and Technology Commission (Z171100001017084), Natural Science Foundation of Beijing Municipality (7171013 and H2018206423), Key Program of National Natural Science Foundation of China (81730004), the National Natural Science Foundation of China (81670116 and 81970113), and National Key Research and Development Program of China (2017YFA0105503).


Interest statement

The author(s) declare that they have no conflict of interest.


Supplementary data

SUPPORTING INFORMATION

The supporting information is available online at https://doi.org/10.1007/s11427-020-1788-2. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.


References

[1] Albertsen M., Hugenholtz P., Skarshewski A., Nielsen K.L., Tyson G.W., Nielsen P.H.. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol, 2013, 31533-538 CrossRef PubMed Google Scholar

[2] Asahi A., Nishimoto T., Okazaki Y., Suzuki H., Masaoka T., Kawakami Y., Ikeda Y., Kuwana M.. Helicobacter pylori eradication shifts monocyte Fcγ receptor balance toward inhibitory FcγRIIB in immune thrombocytopenic purpura patients. J Clin Invest, 2008, 1182939-2949 CrossRef PubMed Google Scholar

[3] Audia S., Mahévas M., Samson M., Godeau B., Bonnotte B.. Pathogenesis of immune thrombocytopenia. Autoimmun Rev, 2017, 16620-632 CrossRef PubMed Google Scholar

[4] Azzouz D., Omarbekova A., Heguy A., Schwudke D., Gisch N., Rovin B.H., Caricchio R., Buyon J.P., Alekseyenko A.V., Silverman G.J.. Lupus nephritis is linked to disease-activity associated expansions and immunity to a gut commensal. Ann Rheum Dis, 2019, 78947-956 CrossRef PubMed Google Scholar

[5] Bettaieb A., Oksenhendler E., Duedari N., Bierling P.. Cross-reactive antibodies between HIV-gp120 and platelet gpIIIa (CD61) in HIV-related immune thrombocytopenic purpura. Clin Exp Immunol, 1996, 10319-23 CrossRef PubMed Google Scholar

[6] Borody T., Campbell J., Torres M., Nowak A., Leis S.. Reversal of idiopathic thrombocytopenic purpura [ITP] with fecal microbiota transplantation [FMT]. Am J Gastroenterol, 2011, 106S352 CrossRef Google Scholar

[7] Brandt L.J., Aroniadis O.C., Mellow M., Kanatzar A., Kelly C., Park T., Stollman N., Rohlke F., Surawicz C.. Long-term follow-up of colonoscopic fecal microbiota transplant for recurrent Clostridium difficile infection. Am J Gastroenterol, 2012, 1071079-1087 CrossRef PubMed Google Scholar

[8] Breunis W.B., van Mirre E., Bruin M., Geissler J., de Boer M., Peters M., Roos D., de Haas M., Koene H.R., Kuijpers T.W.. Copy number variation of the activating FCGR2C gene predisposes to idiopathic thrombocytopenic purpura. Blood, 2008, 1111029-1038 CrossRef PubMed Google Scholar

[9] Chiang J.Y.L.. Bile acids: regulation of synthesis. J Lipid Res, 2009, 501955-1966 CrossRef PubMed Google Scholar

[10] Chua H.H., Chou H.C., Tung Y.L., Chiang B.L., Liao C.C., Liu H.H., Ni Y.H.. Intestinal dysbiosis featuring abundance of Ruminococcus gnavus associates with allergic diseases in infants. Gastroenterology, 2018, 154154-167 CrossRef PubMed Google Scholar

[11] Clemente J.C., Ursell L.K., Parfrey L.W., Knight R.. The impact of the gut microbiota on human health: an integrative view. Cell, 2012, 1481258-1270 CrossRef PubMed Google Scholar

[12] Collison J.. Gut microbiota linked to kidney disease in SLE. Nat Rev Rheumatol, 2019, 15188 CrossRef PubMed Google Scholar

[13] Cooper N., Ghanima W.. Immune thrombocytopenia. N Engl J Med, 2019, 381945-955 CrossRef PubMed Google Scholar

[14] Dao M.C., Everard A., Aron-Wisnewsky J., Sokolovska N., Prifti E., Verger E.O., Kayser B.D., Levenez F., Chilloux J., Hoyles L., et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut, 2016, 65426-436 CrossRef PubMed Google Scholar

[15] Derrien M., Belzer C., de Vos W.M.. Akkermansia muciniphila and its role in regulating host functions. Microb Pathogenesis, 2017, 106171-181 CrossRef PubMed Google Scholar

[16] Eckburg P.B., Bik E.M., Bernstein C.N., Purdom E., Dethlefsen L., Sargent M., Gill S.R., Nelson K.E., Relman D.A.. Diversity of the human intestinal microbial flora. Science, 2005, 3081635-1638 CrossRef PubMed ADS Google Scholar

[17] Fiorucci S., Distrutti E.. Bile acid-activated receptors, intestinal microbiota, and the treatment of metabolic disorders. Trends Mol Med, 2015, 21702-714 CrossRef PubMed Google Scholar

[18] Forslund K., Hildebrand F., Nielsen T., Falony G., Le Chatelier E., Sunagawa S., Prifti E., Vieira-Silva S., Gudmundsdottir V., Krogh Pedersen H., et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 2015, 528262-266 CrossRef PubMed ADS Google Scholar

[19] Francis O.E., Bendall M., Manimaran S., Hong C., Clement N.L., Castro-Nallar E., Snell Q., Schaalje G.B., Clement M.J., Crandall K.A., et al. Pathoscope: Species identification and strain attribution with unassembled sequencing data. Genome Res, 2013, 231721-1729 CrossRef PubMed Google Scholar

[20] Franzosa E.A., Sirota-Madi A., Avila-Pacheco J., Fornelos N., Haiser H.J., Reinker S., Vatanen T., Hall A.B., Mallick H., McIver L.J., et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol, 2019, 4293-305 CrossRef PubMed Google Scholar

[21] Frydman G.H., Davis N., Beck P.L., Fox J.G.. Helicobacter pylori eradication in patients with immune thrombocytopenic purpura: A review and the role of biogeography. Helicobacter, 2015, 20239-251 CrossRef PubMed Google Scholar

[22] Fu L., Niu B., Zhu Z., Wu S., Li W.. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 283150-3152 CrossRef PubMed Google Scholar

[23] Furumitsu, Y., Yukioka, K., Yukioka, M., Ochi, T., Morishima, Y., Matsui-Yuasa, I., Otani, S., Inaba, M., Nishizawa, Y., and Morii, H. (2000). Interleukin-1beta induces elevation of spermidine/spermine N1-acetyltransferase activity and an increase in the amount of putrescine in synovial adherent cells from patients with rheumatoid arthritis. J Rheumatol 27, 1352–1357. Google Scholar

[24] Furusawa Y., Obata Y., Fukuda S., Endo T.A., Nakato G., Takahashi D., Nakanishi Y., Uetake C., Kato K., Kato T., et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature, 2013, 504446-450 CrossRef PubMed ADS Google Scholar

[25] Goleva E., Harris J.K., Robertson C.E., Jackson L.P., Martin R.J., Leung D.Y.M.. Airway microbiome and responses to corticosteroids in corticosteroid-resistant asthma patients treated with acid suppression medications. J Allergy Clin Immunol, 2017, 140860-862.e1 CrossRef PubMed Google Scholar

[26] Goleva E., Jackson L.P., Harris J.K., Robertson C.E., Sutherland E.R., Hall C.F., Good Jr. J.T., Gelfand E.W., Martin R.J., Leung D.Y.M.. The effects of airway microbiome on corticosteroid responsiveness in asthma. Am J Respir Crit Care Med, 2013, 1881193-1201 CrossRef PubMed Google Scholar

[27] He Y., Xu L.L., Feng F.E., Wang Q.M., Zhu X.L., Wang C.C., Zhang J.M., Fu H.X., Xu L.P., Liu K.Y., et al. Mesenchymal stem cell deficiency influences megakaryocytopoiesis through the TNFAIP3/NF-κB/SMAD pathway in patients with immune thrombocytopenia. Br J Haematol, 2018, 180395-411 CrossRef PubMed Google Scholar

[28] Hevia A., Milani C., López P., Cuervo A., Arboleya S., Duranti S., Turroni F., González S., Suárez A., Gueimonde M., et al. Intestinal dysbiosis associated with systemic lupus erythematosus. mBio, 2014, 5 CrossRef PubMed Google Scholar

[29] Huson D.H., Auch A.F., Qi J., Schuster S.C.. MEGAN analysis of metagenomic data. Genome Res, 2007, 17377-386 CrossRef PubMed Google Scholar

[30] Imhann F., Bonder M.J., Vich Vila A., Fu J., Mujagic Z., Vork L., Tigchelaar E.F., Jankipersadsing S.A., Cenit M.C., Harmsen H.J.M., et al. Proton pump inhibitors affect the gut microbiome. Gut, 2016, 65740-748 CrossRef PubMed Google Scholar

[31] Iwamura C., Bouladoux N., Belkaid Y., Sher A., Jankovic D.. Sensing of the microbiota by NOD1 in mesenchymal stromal cells regulates murine hematopoiesis. Blood, 2017, 129171-176 CrossRef PubMed Google Scholar

[32] Jackson M.A., Goodrich J.K., Maxan M.E., Freedberg D.E., Abrams J.A., Poole A.C., Sutter J.L., Welter D., Ley R.E., Bell J.T., et al. Proton pump inhibitors alter the composition of the gut microbiota. Gut, 2016, 65749-756 CrossRef PubMed Google Scholar

[33] Jackson S., Beck P.L., Pineo G.F., Poon M.C.. Helicobacter pylori eradication: novel therapy for immune thrombocytopenic purpura? A review of the literature. Am J Hematol, 2005, 78142-150 CrossRef PubMed Google Scholar

[34] Jie Z., Xia H., Zhong S.L., Feng Q., Li S., Liang S., Zhong H., Liu Z., Gao Y., Zhao H., et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat Commun, 2017, 8845 CrossRef PubMed ADS Google Scholar

[35] Kinnebrew M.A., Buffie C.G., Diehl G.E., Zenewicz L.A., Leiner I., Hohl T.M., Flavell R.A., Littman D.R., Pamer E.G.. Interleukin 23 production by intestinal CD103+CD11b+ dendritic cells in response to bacterial flagellin enhances mucosal innate immune defense. Immunity, 2012, 36276-287 CrossRef PubMed Google Scholar

[36] Kong Y., Cao X.N., Zhang X.H., Shi M.M., Lai Y.Y., Wang Y., Xu L.P., Chang Y.J., Huang X.J.. Atorvastatin enhances bone marrow endothelial cell function in corticosteroid-resistant immune thrombocytopenia patients. Blood, 2018, 1311219-1233 CrossRef PubMed Google Scholar

[37] Kuwana M., Asahi A., Suzuki H., Okazaki Y., Ikeda Y.. Eradication of Helicobacter pylori shifts the balance of Fcγ receptors on monocytes toward the inhibitory FcγRIIB in patients with chronic ITP. Blood, 2006, 1081082 CrossRef Google Scholar

[38] Le Chatelier E., Nielsen T., Qin J., Prifti E., Hildebrand F., Falony G., Almeida M., Arumugam M., Batto J.M., Kennedy S., et al. Richness of human gut microbiome correlates with metabolic markers. Nature, 2013, 500541-546 CrossRef PubMed ADS Google Scholar

[39] Lewis J.D., Chen E.Z., Baldassano R.N., Otley A.R., Griffiths A.M., Lee D., Bittinger K., Bailey A., Friedman E.S., Hoffmann C., et al. Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn’s disease. Cell Host Microbe, 2015, 18489-500 CrossRef PubMed Google Scholar

[40] Li J., Ma S., Shao L., Ma C., Gao C., Zhang X.H., Hou M., Peng J.. Inflammation-related gene polymorphisms associated with primary immune thrombocytopenia. Front Immunol, 2017, 8 CrossRef PubMed Google Scholar

[41] Li R., Yu C., Li Y., Lam T.W., Yiu S.M., Kristiansen K., Wang J.. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 2009, 251966-1967 CrossRef PubMed Google Scholar

[42] Li W., Godzik A.. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 221658-1659 CrossRef PubMed Google Scholar

[43] Liu C., Cheng L., Ji L., Li F., Zhan Y., Wu B., Ke Y., Chen P., Hua F., Yuan L., et al. Intestinal microbiota dysbiosis play a role in pathogenesis of patients with primary immune thrombocytopenia. Thromb Res, 2020, 19011-19 CrossRef PubMed Google Scholar

[44] Liu R., Hong J., Xu X., Feng Q., Zhang D., Gu Y., Shi J., Zhao S., Liu W., Wang X., et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat Med, 2017, 23859-868 CrossRef PubMed Google Scholar

[45] Luo Y., Chen G.L., Hannemann N., Ipseiz N., Krönke G., Bäuerle T., Munos L., Wirtz S., Schett G., Bozec A.. Microbiota from obese mice regulate hematopoietic stem cell differentiation by altering the bone niche. Cell Metab, 2015, 22886-894 CrossRef PubMed Google Scholar

[46] Malnick S.D.H., Oppenheim A., Melzer E.. Immune thrombocytopenia caused by fecal microbial transplantation in a patient with severe recurrent clostridium difficile infection. J Clin Gastroenterol, 2015, 49888-889 CrossRef PubMed Google Scholar

[47] Manzo V.E., Bhatt A.S.. The human microbiome in hematopoiesis and hematologic disorders. Blood, 2015, 126311-318 CrossRef PubMed Google Scholar

[48] Maynard C.L., Elson C.O., Hatton R.D., Weaver C.T.. Reciprocal interactions of the intestinal microbiota and immune system. Nature, 2012, 489231-241 CrossRef PubMed ADS Google Scholar

[49] McMillan R., Nugent D.. The effect of antiplatelet autoantibodies on megakaryocytopoiesis. Int J Hematol, 2005, 8194-99 CrossRef PubMed Google Scholar

[50] Miquel S., Martín R., Rossi O., Bermúdez-Humarán L.G., Chatel J.M., Sokol H., Thomas M., Wells J.M., Langella P.. Faecalibacterium prausnitzii and human intestinal health. Curr Opin Microbiol, 2013, 16255-261 CrossRef PubMed Google Scholar

[51] Moreno B., Fernandez-Diez B., Di Penta A., Villoslada P.. Preclinical studies of methylthioadenosine for the treatment of multiple sclerosis. Mult Scler, 2010, 161102-1108 CrossRef PubMed Google Scholar

[52] Nielsen H.B., Almeida M., Juncker A.S., Rasmussen S., Li J., Sunagawa S., Plichta D.R., Gautier L., Pedersen A.G., Le Chatelier E., et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol, 2014, 32822-828 CrossRef PubMed Google Scholar

[53] Nomura S., Matsuzaki T., Ozaki Y., Yamaoka M., Yoshimura C., Katsura K., Xie G.L., Kagawa H., Ishida T., Fukuhara S.. Clinical significance of HLA-DRB1*0410 in Japanese patients with idiopathic thrombocytopenic purpura. Blood, 1998, 913616-3622 CrossRef Google Scholar

[54] Patil K.R., Nielsen J.. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci USA, 2005, 1022685-2689 CrossRef PubMed ADS Google Scholar

[55] Plaza-Díaz J., Ruiz-Ojeda F.J., Vilchez-Padial L.M., Gil A.. Evidence of the anti-inflammatory effects of probiotics and synbiotics in intestinal chronic diseases. Nutrients, 2017, 9555 CrossRef PubMed Google Scholar

[56] Png C.W., Lindén S.K., Gilshenan K.S., Zoetendal E.G., McSweeney C.S., Sly L.I., McGuckin M.A., Florin T.H.J.. Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol, 2010, 1052420-2428 CrossRef PubMed Google Scholar

[57] Provan D., Arnold D.M., Bussel J.B., Chong B.H., Cooper N., Gernsheimer T., Ghanima W., Godeau B., González-López T.J., Grainger J., et al. Updated international consensus report on the investigation and management of primary immune thrombocytopenia. Blood Adv, 2019, 33780-3817 CrossRef PubMed Google Scholar

[58] Provan D., Stasi R., Newland A.C., Blanchette V.S., Bolton-Maggs P., Bussel J.B., Chong B.H., Cines D.B., Gernsheimer T.B., Godeau B., et al. International consensus report on the investigation and management of primary immune thrombocytopenia. Blood, 2010, 115168-186 CrossRef PubMed Google Scholar

[59] Qi X., Li X., Zhao Y., Wu X., Chen F., Ma X., Zhang F., Wu D.. Treating steroid refractory intestinal acute graft-vs.-host disease with fecal microbiota transplantation: A pilot study. Front Immunol, 2018, 92195 CrossRef PubMed Google Scholar

[60] Qin J., Li Y., Cai Z., Li S., Zhu J., Zhang F., Liang S., Zhang W., Guan Y., Shen D., et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 2012, 49055-60 CrossRef PubMed ADS Google Scholar

[61] Qin J., Li R., Raes J., Arumugam M., Burgdorf K.S., Manichanh C., Nielsen T., Pons N., Levenez F., Yamada T., et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 2010, 46459-65 CrossRef PubMed ADS Google Scholar

[62] Qin N., Yang F., Li A., Prifti E., Chen Y., Shao L., Guo J., Le Chatelier E., Yao J., Wu L., et al. Alterations of the human gut microbiome in liver cirrhosis. Nature, 2014, 51359-64 CrossRef PubMed ADS Google Scholar

[63] Rooks M.G., Garrett W.S.. Gut microbiota, metabolites and host immunity. Nat Rev Immunol, 2016, 16341-352 CrossRef PubMed Google Scholar

[64] Satoh T., Miyazaki K., Shimohira A., Amano N., Okazaki Y., Nishimoto T., Akahoshi T., Munekata S., Kanoh Y., Ikeda Y., et al. Fcγ receptor IIB gene polymorphism in adult Japanese patients with primary immune thrombocytopenia. Blood, 2013, 1221991-1992 CrossRef PubMed Google Scholar

[65] Sokol H., Pigneur B., Watterlot L., Lakhdari O., Bermúdez-Humarán L.G., Gratadoux J.J., Blugeon S., Bridonneau C., Furet J.P., Corthier G., et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci USA, 2008, 10516731-16736 CrossRef PubMed ADS Google Scholar

[66] Stat M., Pochon X., Franklin E.C., Bruno J.F., Casey K.S., Selig E.R., Gates R.D.. The distribution of the thermally tolerant symbiont lineage (Symbiodinium clade D) in corals from Hawaii: correlations with host and the history of ocean thermal stress. Ecol Evol, 2013, 31317-1329 CrossRef PubMed Google Scholar

[67] Suzuki T., Matsushima M., Masui A., Watanabe K., Takagi A., Ogawa Y., Shirai T., Mine T.. Effect of Helicobacter pylori eradication in patients with chronic idiopathic thrombocytopenic purpura—A randomized controlled trial. Am J Gastroenterol, 2005, 1001265-1270 CrossRef PubMed Google Scholar

[68] Takahashi T., Yujiri T., Shinohara K., Inoue Y., Sato Y., Fujii Y., Okubo M., Zaitsu Y., Ariyoshi K., Nakamura Y., et al. Molecular mimicry by Helicobacter pylori CagA protein may be involved in the pathogenesis of H. pylori-associated chronic idiopathic thrombocytopenic purpura. Br J Haematol, 2004, 12491-96 CrossRef PubMed Google Scholar

[69] Thomas T.J., Meryhew N.L., Messner R.P.. Enhanced binding of lupus sera to the polyamine-induced left-handed Z-DNA form of polynucleotides. Arthritis Rheum, 1990, 33356-365 CrossRef PubMed Google Scholar

[70] Tsutsumi Y., Kanamori H., Yamato H., Ehira N., Kawamura T., Umehara S., Mori A., Obara S., Ogura N., Tanaka J., et al. Randomized study of Helicobacter pylori eradication therapy and proton pump inhibitor monotherapy for idiopathic thrombocytopenic purpura. Ann Hematol, 2005, 84807-811 CrossRef PubMed Google Scholar

[71] Wang Q., Li J., Yu T.S., Liu Y., Li K., Liu S., Liu Y., Feng Q., Zhang L., Li G.S., et al. Disrupted balance of CD4+ T-cell subsets in bone marrow of patients with primary immune thrombocytopenia. Int J Biol Sci, 2019, 152798-2814 CrossRef PubMed Google Scholar

[72] Wang, T., Zhao, H., Ren, H., Guo, J., Xu, M., Yang, R., and Han, Z.C. (2005). Type 1 and type 2 T-cell profiles in idiopathic thrombocytopenic purpura. Haematologica 90, 914–923. Google Scholar

[73] Wen C., Zheng Z., Shao T., Liu L., Xie Z., Le Chatelier E., He Z., Zhong W., Fan Y., Zhang L., et al. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis. Genome Biol, 2017, 18142 CrossRef PubMed Google Scholar

[74] Wu H., Esteve E., Tremaroli V., Khan M.T., Caesar R., Mannerås-Holm L., Ståhlman M., Olsson L.M., Serino M., Planas-Fèlix M., et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med, 2017, 23850-858 CrossRef PubMed Google Scholar

[75] Xing J., Ying Y., Mao C., Liu Y., Wang T., Zhao Q., Zhang X., Yan F., Zhang H.. Hypoxia induces senescence of bone marrow mesenchymal stem cells via altered gut microbiota. Nat Commun, 2018, 92020 CrossRef PubMed ADS Google Scholar

[76] Zhang J.M., Feng F.E., Wang Q.M., Zhu X.L., Fu H.X., Xu L.P., Liu K.Y., Huang X.J., Zhang X.H.. Platelet-derived growth factor-BB protects mesenchymal stem cells (MSCs) derived from immune thrombocytopenia patients against apoptosis and senescence and maintains MSC-mediated immunosuppression. Stem Cells Transl Med, 2016, 51631-1643 CrossRef PubMed Google Scholar

[77] Zhang W., Nardi M.A., Borkowsky W., Li Z., Karpatkin S.. Role of molecular mimicry of hepatitis C virus protein with platelet GPIIIa in hepatitis C-related immunologic thrombocytopenia. Blood, 2009, 1134086-4093 CrossRef PubMed Google Scholar

[78] Zhang X., Zhang D., Jia H., Feng Q., Wang D., Liang D., Wu X., Li J., Tang L., Li Y., et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat Med, 2015, 21895-905 CrossRef PubMed Google Scholar

[79] Zhu W., Lomsadze A., Borodovsky M.. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res, 2010, 38e132 CrossRef PubMed Google Scholar

  • Figure 1

    Gut microbial alterations in ITP individuals. Comparison between treatment-naïve ITP patients (n=49) and healthy controls (n=52). A, Rarefaction curves were constructed with 97% sequence similarity at species level. B, The number of annotated species showed no difference between the two groups (P>0.05). C and D, α-Diversity (Shannon index) of the two groups at the gene level (P>0.05) and the species level (P>0.05). E and F, β-Diversity (Bray distance) of the two groups at the gene level (P<0.05) and the species level (P<0.001). G, The distribution of P values. The genes whose median abundances in two cohorts were both less than 10−7 were filtered. After filtering, Wilcoxon rank-sun test was applied to identify the differentially abundant genes. Frequency histogram shows the P value distribution of all genes tested in the two groups. H, The PCA was built using the genes that differed significantly between treatment-naïve ITP patients and healthy controls (false discovery rate (FDR), 0.0001, Wilcoxon rank-sum test adjusted for multiple testing). Forty-nine patients with treatment-naïve ITP are in red, and 52 healthy controls are in blue. I, The of Bacteroidetes/Firmicutes ratio in healthy controls and treatment-ITP patients was similar (the P value shows no obvious significance). For the box plot, Two-tailed Wilcoxon rank-sum test was used to determine significance; boxes represent the interquartile ranges (IQRs) between the first and third quartiles, and the line inside the box represents the median; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. “+” represent data point beyond the whiskers. The notches show the 95% confidence interval for the medians.

  • Figure 2

    Differences in phylogenetic abundance between treatment-naïve ITP patients and healthy controls. A, B and D, The phylotypes that were increased in the treatment-naïve ITP patients at the phylum level, genus level and species level. Red and blue indicate the ITP patients and healthy controls, respectively. C and E, The phylotypes that were decreased in the treatment-naïve ITP patients at the genus level and species level. The phylogenetic abundance of phyla that had mean values less than 1% and that of genera and species that were less than 0.01% were excluded. After exclusion, Wilcoxon rank-sum tests were applied to identify the differentially abundant phyla, genera, and species. Among these, the highest medians of the phylogenetic abundance in the enriched cohort were drawn as boxplots.

  • Figure 3

    Correlation between gut species and ITP-related clinical indices, and trial classification of ITP using gut microbial markers. A, The abundance of gut species differed between treatment-naïve ITP patients and healthy controls were analyzed for covariation with clinical variables using Spearman’s correlation coefficient. Species and phenotypes in the heatmap were ordered using unsupervised hierarchical clustering. B and C, The area under the ROC curve (AUC) of gut-microbiota-based ITP classification. A classifier to identify treatment-naïve ITP patients was constructed using 12 gut microbial markers selected by random forest model. The AUC based the classifier is shown for the training and test samples. D and E, The AUC of ITP-associated MGS classification. A classifier based on 22 MGSs selected by random forest model was constructed. The AUC based the classifier is shown for the training and test samples. The gray bars denote the 95% confidence interval (CI) and the area between the two outside curves represents the 95%CI shape. Abbreviations: WBC: white blood cell count; LDL: low density lipoprotein; BMI: body mass index; HDL: high density lipoprotein; ALT: alanine aminotransferase; AST: aspartate aminotransferase; +, P<0.05; *, P<0.01; #, P<0.001.

  • Figure 4

    Co-occurrence network deduced from 69 MGSs enriched in treatment-naïve ITP patients and healthy controls. MGSs were colored according to the phylum they were annotated to. Sizes of the nodes represent the number of genes in the MGSs (700–7,099). Red edges, Spearman’s rank correlation coefficient>0.6, adjusted P<0.05; blue edges, Spearman’s rank correlation coefficient<−0.3, adjusted P<0.05. Unclassified MGSs could not be annotated to any taxonomic level as a result of the low gene annotation. The numbers in parentheses next to each species name represent unique MGS identifiers.

  • Figure 5

    Functional characterization of the treatment-naïve ITP microbiome. The relative abundances of KEGG pathways (A) and modules (B) were compared between treatment-naïve ITP patients and healthy controls (ITP, n=49; healthy controls, n=52). KEGG pathways or modules with reporter scores>1.65 or <−1.65 are shown. Blue and red colors represent healthy control- and ITP-enriched pathways or modules, respectively.

  • Figure 6

    Prediction of the effects of ITP medication on the gut microbiome by random forest classifiers. A–D, Random forest classifiers were used to separate the gut microbiomes of Drug+ ITP patients, Drug− ITP patients and healthy controls, and receiver operating characteristic (ROC) curves are shown for corticosteroid, danazol, TPO/TPO-RA and amino-polypeptide. Ten-fold cross-validation with a random forest classifier was performed ten times. Red curve, Drug− patients versus Drug+ patients. Green curve, Drug− patients versus controls. Blue curve, Drug+ patients versus controls. E, The area under the curve (AUC) and 95%CI are shown.

  • Figure 7

    Gut microbiome alterations in corticosteroid-resistant ITP patients. A and B, Phylogenetic abundance at genus level and species level between corticosteroid-resistant and corticosteroid-sensitive ITP patients and healthy controls. Boxes represent the median and interquartile ranges (IQRs) between the first and third quartiles; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. “o” represents all data points. C and D, The relative abundances of KEGG pathways and modules were compared between corticosteroid-resistant and corticosteroid-sensitive ITP. KEGG pathways and modules with reporter scores>1.65 or <−1.65 are shown.

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